Variance reduction is an important tool to increase the rate of convergence in certainconfigurations of Monte Carlo problems. Methods such as CADIS are particularly useful to achieve this increased rate of convergence. However, CADIS does not include information for direction phase space, and an equivalent method has not been used for the adjoint Monte Carlo method. In this work, the benefits of including direction information in a weight window and weight target (a new type of importance sampling technique presented here) are analyzed and explored, along with a way to use importance sampling theory on the adjoint Monte Carlo method.